Gachon University - 2023 Data Science Final Projects
The goal of this project was to experience the Big Data End-to-End Process, which can predict the extent of damage if a fire breaks out in a forest in Korea.

• Decision Tree
• K-Nearest Neighbors
• Random Forest
• AdaBoost
• Gradient Boosting
• Voting Classifier
• Bagging Classifier with Decision Tree Stump
• K-Means Clustering

$ pip install -r requirment.txt

To prepare a dataset for training, you need to merge the datasets.

$ python .\dataset\preprocess\create_dataset.py

The experiment will be customized to your configuration.

$ python evaluate.py

• data_path: str.
  The path to the dataset for training and testing.
• param_path: str.
  The path to the file where all configuration and parameters are written.
• seed: int.
  random_state, a random seed.
• redirector: bool.
  Whether to log learning results.
• visual: bool.
  Visualize learning outcomes
• model: str.
  Models to use for training 'dt'(decision tree), 'lr'(logistic regression), 'knn'(k-nearest neighbors), 'rf'(random forest), 'ab'(AdaBoost), 'gb'(gradient boosting), 'voting' (voting classifier), 'bag' (bagging classifier) 'kmeans' (k-means clustering)
• param_load: bool.
  Whether to load the tuned hyperparameter.
• voting_list: list.
  A list of models to include in the Voting Classifier.
• tune: str.
  The method for finding grid, random, None, and hyperparameters.
• n_iter: int.
  The maximum number of iterations for random tuning.
• cv: int.
  This is the parameter of the K-Fold when tuning.
• pca: bool.
  Whether to use the PCA technique.
• n_components: float.
  The minimum explained_variance_ratio in PCA.
• smote: bool.
  Data Augmentation, whether to use SMOTE.
• standard: bool.
  Whether to apply StandardScaler to the dataset before training.
• eval: str.
  Validation technique options are kfold, holdout, kfold_stratified.
• n_split: int.
  The number you want to divide by in kfold.
• num_class: int.
  The number of classes

$ python all_eval.py

23_Data_Science
|   config.py
|   main.py
|   all_eval.py
|   evaluate.py
|   requirements.txt
|   
+---dataset
|   |   Dataset.py
|   |   FireDataset.csv
|   |   preprocessed.csv
|   |   
|   +---Fire
|   |       FireFacility_latlong.csv
|   |       FireStationPos.csv
|   |       FireStation_latlong.csv
|   |       FireStatistic.csv
|   |       FireStatistic_latlong.csv
|   |       MountainHeight.xlsx
|   |       MountainHeight_latlong.csv
|   |       
|   +---preprocess
|   |   |   create_dataset.py
|   |   |   preprocessing.py
|   |   |   
|   |   +---functions
|           
+---logs
|       stdout_2023-06-04_13_47_25.txt
|       
+---models
|   |   model.py
|   |   
|   +---config
|   |       ab_range.txt
|   |       dt_range.txt
|   |       gb_range.txt
|   |       knn_range.txt
|   |       lr_range.txt
|   |       rf_range.txt
|   |       voting_range.txt
|           
+---tools